YOLOv5 分类模型 Top 1和Top 5 指标实现

flyfish

import time
from models.common import DetectMultiBackend
import os
import os.path
from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Union
import cv2
import numpy as npimport torch
from utils.augmentations import classify_transformsclass DatasetFolder:def __init__(self,root: str,) -> None:self.root = rootclasses, class_to_idx = self.find_classes(self.root)samples = self.make_dataset(self.root, class_to_idx)self.classes = classesself.class_to_idx = class_to_idxself.samples = samplesself.targets = [s[1] for s in samples]@staticmethoddef make_dataset(directory: str,class_to_idx: Optional[Dict[str, int]] = None,) -> List[Tuple[str, int]]:directory = os.path.expanduser(directory)if class_to_idx is None:_, class_to_idx = self.find_classes(directory)elif not class_to_idx:raise ValueError("'class_to_index' must have at least one entry to collect any samples.")instances = []available_classes = set()for target_class in sorted(class_to_idx.keys()):class_index = class_to_idx[target_class]target_dir = os.path.join(directory, target_class)if not os.path.isdir(target_dir):continuefor root, _, fnames in sorted(os.walk(target_dir, followlinks=True)):for fname in sorted(fnames):path = os.path.join(root, fname)if 1:  # 验证:item = path, class_indexinstances.append(item)if target_class not in available_classes:available_classes.add(target_class)empty_classes = set(class_to_idx.keys()) - available_classesif empty_classes:msg = f"Found no valid file for the classes {', '.join(sorted(empty_classes))}. "return instancesdef find_classes(self, directory: str) -> Tuple[List[str], Dict[str, int]]:classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir())if not classes:raise FileNotFoundError(f"Couldn't find any class folder in {directory}.")class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}return classes, class_to_idxdef __getitem__(self, index: int) -> Tuple[Any, Any]:path, target = self.samples[index]sample = self.loader(path)return sample, targetdef __len__(self) -> int:return len(self.samples)def loader(self, path):print("path:", path)img = cv2.imread(path)  # BGR HWCreturn imgdef time_sync():return time.time()dataset = DatasetFolder(root="/media/flyfish/test/val")# image, label=dataset[7]
# print(image.shape)
#
weights = "/media/flyfish/yolov5-6.2/classes10.pt"
device = "cpu"
model = DetectMultiBackend(weights, device=device, dnn=False, fp16=False)
model.eval()transforms = classify_transforms(224)pred, targets, loss, dt = [], [], 0, [0.0, 0.0, 0.0]
# current batch size =1
for i, (images, labels) in enumerate(dataset):print("i:", i)print(images.shape, labels)im = cv2.cvtColor(images, cv2.COLOR_BGR2RGB)im = transforms(im)images = im.unsqueeze(0).to("cpu")print(images.shape)t1 = time_sync()images = images.to(device, non_blocking=True)t2 = time_sync()# dt[0] += t2 - t1y = model(images)y=y.numpy()print("y:", y)t3 = time_sync()# dt[1] += t3 - t2tmp1=y.argsort()[:,::-1][:, :5]print("tmp1:", tmp1)pred.append(tmp1)print("labels:", labels)targets.append(labels)print("for pred:", pred)  # listprint("for targets:", targets)  # list# dt[2] += time_sync() - t3pred, targets = np.concatenate(pred), np.array(targets)
print("pred:", pred)
print("pred:", pred.shape)
print("targets:", targets)
print("targets:", targets.shape)
correct = ((targets[:, None] == pred)).astype(np.float32)
print("correct:", correct.shape)
print("correct:", correct)
acc = np.stack((correct[:, 0], correct.max(1)), axis=1)  # (top1, top5) accuracy
print("acc:", acc.shape)
print("acc:", acc)
top = acc.mean(0)
print("top1:", top[0])
print("top5:", top[1])

输出

pred: [[7 4 0 5 9][9 2 4 6 7][8 9 6 2 1][8 9 6 2 7][9 2 4 6 3][6 7 1 2 9][4 2 1 8 9][6 8 9 5 2][8 7 4 2 6][9 8 2 6 4][2 9 8 0 6][7 4 8 6 3]]
pred: (12, 5)
targets: [0 0 0 0 1 1 1 1 2 2 2 2]
targets: (12,)
correct: (12, 5)
correct: [[          0           0           1           0           0][          0           0           0           0           0][          0           0           0           0           0][          0           0           0           0           0][          0           0           0           0           0][          0           0           1           0           0][          0           0           1           0           0][          0           0           0           0           0][          0           0           0           1           0][          0           0           1           0           0][          1           0           0           0           0][          0           0           0           0           0]]
acc: (12, 2)
acc: [[          0           1][          0           0][          0           0][          0           0][          0           0][          0           1][          0           1][          0           0][          0           1][          0           1][          1           1][          0           0]]
top1: 0.083333336
top5: 0.5

Yolov5 6.2 原版输出

pred: tensor([[6, 7, 1, 2, 9],[9, 2, 4, 6, 3],[7, 4, 0, 5, 9],[9, 8, 2, 6, 4],[6, 8, 9, 5, 2],[8, 7, 4, 2, 6],[9, 2, 4, 6, 7],[2, 9, 8, 0, 6],[8, 9, 6, 2, 7],[7, 4, 8, 6, 3],[4, 2, 1, 8, 9],[8, 9, 6, 2, 1]])
pred: torch.Size([12, 5])
targets: tensor([1, 1, 0, 2, 1, 2, 0, 2, 0, 2, 1, 0])
targets: torch.Size([12])
correct: torch.Size([12, 5])
acc: torch.Size([12, 2])
top1: 0.0833333358168602
top5: 0.5

文本代码是按照标签，即文件夹名字排序的，pred和target都是一一对应的，与Yolov5 6.2 原版相同